Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


Our top-notch dedicated system is used to design specialised libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.


PARTNER
Receptor.AI
 
UPACC
Q6GYQ0

UPID:
RGPA1_HUMAN

ALTERNATIVE NAMES:
GAP-related-interacting partner to E12; GTPase-activating Rap/Ran-GAP domain-like 1; Tuberin-like protein 1; p240

ALTERNATIVE UPACC:
Q6GYQ0; A6NMA4; B9EK38; C5NU19; O94960; Q6GYP9; Q6ZT23; Q86YF3; Q86YF5; Q8ND69

BACKGROUND:
Ral GTPase-activating protein subunit alpha-1, identified by alternative names such as GAP-related-interacting partner to E12 and Tuberin-like protein 1, is integral to the RalGAP1 complex. This complex is essential for the activation of RALA and RALB GTPases, influencing cellular dynamics and signaling pathways.

THERAPEUTIC SIGNIFICANCE:
This protein's malfunction is associated with a neurodevelopmental disorder with profound implications for affected individuals. The exploration of Ral GTPase-activating protein subunit alpha-1's function offers promising avenues for developing targeted therapies for this debilitating condition.

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